{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3b09da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Duplicate key in file PosixPath('/usr/local/lib/python3.9/dist-packages/matplotlib/mpl-data/matplotlibrc'), line 801 ('font.family: sans-serif')\n",
      "Duplicate key in file PosixPath('/usr/local/lib/python3.9/dist-packages/matplotlib/mpl-data/matplotlibrc'), line 802 ('font.sans-serif: SimHei')\n",
      "Matplotlib is building the font cache; this may take a moment.\n",
      "2025-06-19 09:44:23.987314: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-06-19 09:44:23.991625: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-06-19 09:44:24.004445: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2025-06-19 09:44:24.024805: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2025-06-19 09:44:24.030862: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-06-19 09:44:24.046618: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-06-19 09:44:25.148952: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages/keras/src/layers/rnn/bidirectional.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 22ms/step - loss: 5043.1167 - mae: 5043.6177 - mse: 77396304.0000 - val_loss: 1252.8728 - val_mae: 1253.3726 - val_mse: 8190351.5000 - learning_rate: 0.0010\n",
      "Epoch 2/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - loss: 1692.6583 - mae: 1693.1581 - mse: 13272157.0000 - val_loss: 1292.6183 - val_mae: 1293.1180 - val_mse: 7033159.0000 - learning_rate: 0.0010\n",
      "Epoch 3/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - loss: 1564.1591 - mae: 1564.6591 - mse: 11098714.0000 - val_loss: 1425.3513 - val_mae: 1425.8508 - val_mse: 8859279.0000 - learning_rate: 0.0010\n",
      "Epoch 4/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 22ms/step - loss: 1514.9004 - mae: 1515.4003 - mse: 10519838.0000 - val_loss: 1620.1268 - val_mae: 1620.6265 - val_mse: 13280952.0000 - learning_rate: 0.0010\n",
      "Epoch 5/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - loss: 1466.9423 - mae: 1467.4421 - mse: 10115278.0000 - val_loss: 1447.8618 - val_mae: 1448.3618 - val_mse: 9151068.0000 - learning_rate: 0.0010\n",
      "Epoch 6/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 22ms/step - loss: 1432.9919 - mae: 1433.4918 - mse: 9675092.0000 - val_loss: 1584.3558 - val_mae: 1584.8557 - val_mse: 11385510.0000 - learning_rate: 0.0010\n",
      "Epoch 7/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - loss: 1400.2267 - mae: 1400.7264 - mse: 9354719.0000 - val_loss: 1604.9910 - val_mae: 1605.4907 - val_mse: 10700575.0000 - learning_rate: 5.0000e-04\n",
      "Epoch 8/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - loss: 1385.1559 - mae: 1385.6558 - mse: 8832709.0000 - val_loss: 1499.1392 - val_mae: 1499.6392 - val_mse: 9712592.0000 - learning_rate: 5.0000e-04\n",
      "Epoch 9/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 24ms/step - loss: 1361.0959 - mae: 1361.5958 - mse: 8596091.0000 - val_loss: 1595.0999 - val_mae: 1595.5999 - val_mse: 11212589.0000 - learning_rate: 5.0000e-04\n",
      "Epoch 10/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - loss: 1345.3279 - mae: 1345.8276 - mse: 8472154.0000 - val_loss: 1516.1497 - val_mae: 1516.6497 - val_mse: 10047978.0000 - learning_rate: 5.0000e-04\n",
      "Epoch 11/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - loss: 1338.0605 - mae: 1338.5603 - mse: 8110382.5000 - val_loss: 1558.8010 - val_mae: 1559.3008 - val_mse: 10728385.0000 - learning_rate: 5.0000e-04\n",
      "Epoch 12/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - loss: 1311.4375 - mae: 1311.9373 - mse: 7763268.0000 - val_loss: 1567.5962 - val_mae: 1568.0955 - val_mse: 10874797.0000 - learning_rate: 2.5000e-04\n",
      "Epoch 13/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - loss: 1304.4564 - mae: 1304.9562 - mse: 7562266.5000 - val_loss: 1583.9718 - val_mae: 1584.4718 - val_mse: 11047890.0000 - learning_rate: 2.5000e-04\n",
      "Epoch 14/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 22ms/step - loss: 1306.4675 - mae: 1306.9675 - mse: 7667450.5000 - val_loss: 1620.0273 - val_mae: 1620.5270 - val_mse: 11932698.0000 - learning_rate: 2.5000e-04\n",
      "Epoch 15/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - loss: 1291.8429 - mae: 1292.3427 - mse: 7400788.5000 - val_loss: 1535.0936 - val_mae: 1535.5935 - val_mse: 10682766.0000 - learning_rate: 2.5000e-04\n",
      "Epoch 16/150\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - loss: 1286.7839 - mae: 1287.2837 - mse: 7206354.5000 - val_loss: 1552.9558 - val_mae: 1553.4554 - val_mse: 10769419.0000 - learning_rate: 2.5000e-04\n",
      "\u001b[1m94/94\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step\n",
      "MSE: 8190350.765803233\n",
      "RMSE: 2861.878887340139\n",
      "MAE: 1253.372471359253\n",
      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "# 设置随机种子确保可复现性\n",
    "np.random.seed(7)\n",
    "tf.random.set_seed(7)\n",
    "\n",
    "# 加载数据\n",
    "Train_data = pd.read_csv('./data/used_car_train_20200313.csv', sep=' ')\n",
    "TestB_data = pd.read_csv('./data/used_car_testB_20200421.csv', sep=' ')\n",
    "\n",
    "# 特征选择\n",
    "numerical_cols = Train_data.select_dtypes(exclude='object').columns\n",
    "feature_cols = [col for col in numerical_cols if col not in ['SaleID', 'name', 'regDate', 'creatDate', \n",
    "                                                                'price', 'model', 'brand', 'regionCode', 'seller']]\n",
    "feature_cols = [col for col in feature_cols if 'Type' not in col]\n",
    "\n",
    "# 提取特征和目标变量\n",
    "X_data = Train_data[feature_cols]\n",
    "Y_data = Train_data['price']\n",
    "X_test = TestB_data[feature_cols]\n",
    "\n",
    "# 数据预处理\n",
    "X_data = X_data.fillna(-1)\n",
    "X_test = X_test.fillna(-1)\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_data_scaled = scaler.fit_transform(X_data)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 重塑数据为LSTM输入格式 [样本数, 时间步, 特征数]\n",
    "X_data_reshaped = X_data_scaled.reshape(X_data_scaled.shape[0], 1, X_data_scaled.shape[1])\n",
    "X_test_reshaped = X_test_scaled.reshape(X_test_scaled.shape[0], 1, X_test_scaled.shape[1])\n",
    "\n",
    "# 划分训练集和验证集\n",
    "train_size = len(X_data_reshaped) - 3000\n",
    "X_train, X_val = X_data_reshaped[:train_size], X_data_reshaped[train_size:]\n",
    "y_train, y_val = Y_data[:train_size], Y_data[train_size:]\n",
    "\n",
    "# 构建增强型LSTM模型\n",
    "model = Sequential([\n",
    "    # 双向LSTM层，增强特征提取能力\n",
    "    tf.keras.layers.Bidirectional(\n",
    "        LSTM(128, return_sequences=True, dropout=0.2, recurrent_dropout=0.1),\n",
    "        input_shape=(X_data_reshaped.shape[1], X_data_reshaped.shape[2])\n",
    "    ),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    # 第二层LSTM\n",
    "    LSTM(64, dropout=0.2, recurrent_dropout=0.1),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    # 多层全连接层\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.3),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    Dense(64, activation='relu'),\n",
    "    Dropout(0.3),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    Dense(32, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    # 输出层（回归任务不需要激活函数）\n",
    "    Dense(1)\n",
    "])\n",
    "\n",
    "# 编译模型\n",
    "model.compile(\n",
    "    optimizer=Adam(learning_rate=0.001),\n",
    "    loss=tf.keras.losses.Huber(),  # 对异常值更鲁棒\n",
    "    metrics=['mae', 'mse']  # 使用回归适用的评估指标\n",
    ")\n",
    "\n",
    "# 设置回调函数\n",
    "callbacks = [\n",
    "    EarlyStopping(patience=15, restore_best_weights=True),  # 早停防止过拟合\n",
    "    ReduceLROnPlateau(factor=0.5, patience=5, min_lr=1e-6)  # 学习率衰减\n",
    "]\n",
    "\n",
    "# 训练模型\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=150,\n",
    "    batch_size=96,\n",
    "    callbacks=callbacks,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# 评估模型\n",
    "y_pred = model.predict(X_val)\n",
    "print('MSE:', mean_squared_error(y_val, y_pred))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(y_val, y_pred)))\n",
    "print('MAE:', mean_absolute_error(y_val, y_pred))\n",
    "\n",
    "# 预测测试集\n",
    "Y_test = model.predict(X_test_reshaped)\n",
    "\n",
    "# 保存结果\n",
    "sub = pd.DataFrame({\n",
    "    'SaleID': TestB_data['SaleID'],\n",
    "    'price': Y_test.flatten()\n",
    "})\n",
    "sub.to_csv('sub_Weighted_improved.csv', index=False)\n",
    "\n",
    "# 绘制训练历史\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Train Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.legend()\n",
    "plt.title('Loss Evolution')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['mae'], label='Train MAE')\n",
    "plt.plot(history.history['val_mae'], label='Validation MAE')\n",
    "plt.legend()\n",
    "plt.title('MAE Evolution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bae56fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams[\"figure.figsize\"] = (25, 10)\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['xtick.labelsize'] = 20\n",
    "plt.rcParams['ytick.labelsize'] = 20\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['lines.linewidth'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89bc7cd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape: (150000, 31)\n",
      "TestB data shape: (50000, 30)\n"
     ]
    }
   ],
   "source": [
    "Train_data = pd.read_csv('./data/used_car_train_20200313.csv', sep=' ')\n",
    "TestB_data = pd.read_csv('./data/used_car_testB_20200421.csv', sep=' ')\n",
    "\n",
    "## 输出数据的大小信息\n",
    "print('Train data shape:',Train_data.shape)\n",
    "print('TestB data shape:',TestB_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "54141387",
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_cols = Train_data.select_dtypes(exclude = 'object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6cdc902",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols = Train_data.select_dtypes(include = 'object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "daeea6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','creatDate','price','model','brand','regionCode','seller']]\n",
    "feature_cols = [col for col in feature_cols if 'Type' not in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dacc7d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提前特征列，标签列构造训练样本和测试样本\n",
    "X_data = Train_data[feature_cols]\n",
    "Y_data = Train_data['price']\n",
    "\n",
    "X_test  = TestB_data[feature_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05add903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.23906e+05, 1.89270e+04, 4.91800e+03, 1.34000e+03, 4.71000e+02,\n",
       "        1.88000e+02, 1.24000e+02, 6.00000e+01, 4.80000e+01, 1.80000e+01]),\n",
       " array([1.10000e+01, 1.00098e+04, 2.00086e+04, 3.00074e+04, 4.00062e+04,\n",
       "        5.00050e+04, 6.00038e+04, 7.00026e+04, 8.00014e+04, 9.00002e+04,\n",
       "        9.99990e+04]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 7500x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dd50cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 缺省值用-1填补\n",
    "\n",
    "X_data = X_data.fillna(-1)\n",
    "X_test = X_test.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50337c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = X_data.values.reshape((X_data.shape[0], 1, X_data.shape[1]))\n",
    "X_test = X_test.values.reshape((X_test.shape[0], 1, X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26802ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1850\n",
       "1          3600\n",
       "2          6222\n",
       "3          2400\n",
       "4          5200\n",
       "          ...  \n",
       "146995     9000\n",
       "146996    38500\n",
       "146997     5990\n",
       "146998     5100\n",
       "146999     5900\n",
       "Name: price, Length: 147000, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x_train,x_val,y_train,y_val\n",
    "train = X_data[:-3000, :]\n",
    "test = X_data[-3000:, :]\n",
    "y_train=Y_data[:-3000]\n",
    "y_val=Y_data[-3000:]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21fd57cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Expected `metrics` argument to be a list, tuple, or dict. Received instead: metrics=accuracy of type <class 'str'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m model\u001b[38;5;241m.\u001b[39madd(Dense(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Compile the model\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43madam\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean_squared_error\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maccuracy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m model\u001b[38;5;241m.\u001b[39msummary()\n\u001b[1;32m     14\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(train, y_train, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m96\u001b[39m, validation_data\u001b[38;5;241m=\u001b[39m(test, y_val), verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/.virtualenvs/basenv/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m~/.virtualenvs/basenv/lib/python3.9/site-packages/keras/src/trainers/compile_utils.py:130\u001b[0m, in \u001b[0;36mCompileMetrics.__init__\u001b[0;34m(self, metrics, weighted_metrics, name, output_names)\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(name\u001b[38;5;241m=\u001b[39mname)\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m metrics \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(metrics, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)):\n\u001b[0;32m--> 130\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    131\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected `metrics` argument to be a list, tuple, or dict. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived instead: metrics=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmetrics\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(metrics)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    133\u001b[0m     )\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weighted_metrics \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m    135\u001b[0m     weighted_metrics, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m    136\u001b[0m ):\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    138\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected `weighted_metrics` argument to be a list, tuple, or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    139\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdict. Received instead: weighted_metrics=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mweighted_metrics\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(weighted_metrics)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    141\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: Expected `metrics` argument to be a list, tuple, or dict. Received instead: metrics=accuracy of type <class 'str'>"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(64, input_shape=(X_data.shape[1], X_data.shape[2])))\n",
    "# model.add(LSTM(32))\n",
    "model.add(Dense(100))\n",
    "model.add(Dense(60))\n",
    "model.add(Dense(13))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics='accuracy')\n",
    "\n",
    "model.summary()\n",
    "\n",
    "history = model.fit(train, y_train, epochs=300, batch_size=96, validation_data=(test, y_val), verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6db9c5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m94/94\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.6939018 ],\n",
       "       [-0.574212  ],\n",
       "       [-0.75587684],\n",
       "       ...,\n",
       "       [-0.7043517 ],\n",
       "       [-0.64075273],\n",
       "       [-0.5984266 ]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat = model.predict(test)\n",
    "inv_yHat = yHat\n",
    "inv_yHat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "129ea4c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147000     6950\n",
       "147001     1600\n",
       "147002     5000\n",
       "147003    12850\n",
       "147004     2500\n",
       "          ...  \n",
       "149995     5900\n",
       "149996     9500\n",
       "149997     7500\n",
       "149998     4999\n",
       "149999     4700\n",
       "Name: price, Length: 3000, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_y = y_val\n",
    "inv_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "258a8990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 86330560.13611759\n",
      "RMSE: 9291.424010135239\n",
      "MAE: 5808.484284125536\n"
     ]
    }
   ],
   "source": [
    "def mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_pred - y_true) / y_true))\n",
    "\n",
    "print('MSE:',metrics.mean_squared_error(inv_yHat, inv_y))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(inv_yHat, inv_y)))\n",
    "print('MAE:',metrics.mean_absolute_error(inv_yHat, inv_y)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52c6089",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test = model.predict(X_test)\n",
    "sub = pd.DataFrame()\n",
    "sub['SaleID'] = TestB_data.SaleID\n",
    "sub['price'] = Y_test\n",
    "sub.to_csv('sub_Weighted3.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a927fc64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape: (150000, 31)\n",
      "TestB data shape: (50000, 30)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">21,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,060</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">793</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m21,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │         \u001b[38;5;34m6,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m)             │         \u001b[38;5;34m6,060\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m)             │           \u001b[38;5;34m793\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m14\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,615</span> (135.21 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m34,615\u001b[0m (135.21 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,615</span> (135.21 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34,615\u001b[0m (135.21 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 28510278.0000 - val_accuracy: 0.0000e+00 - val_loss: 3417715.2500\n",
      "Epoch 2/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 4115939.0000 - val_accuracy: 0.0000e+00 - val_loss: 3100517.5000\n",
      "Epoch 3/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3674829.5000 - val_accuracy: 0.0000e+00 - val_loss: 3561771.2500\n",
      "Epoch 4/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3575874.7500 - val_accuracy: 0.0000e+00 - val_loss: 3275726.5000\n",
      "Epoch 5/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3441595.0000 - val_accuracy: 0.0000e+00 - val_loss: 2538738.7500\n",
      "Epoch 6/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3347701.2500 - val_accuracy: 0.0000e+00 - val_loss: 2428644.7500\n",
      "Epoch 7/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3409927.5000 - val_accuracy: 0.0000e+00 - val_loss: 2787301.2500\n",
      "Epoch 8/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3491849.7500 - val_accuracy: 0.0000e+00 - val_loss: 4364577.0000\n",
      "Epoch 9/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3274597.2500 - val_accuracy: 0.0000e+00 - val_loss: 2452271.2500\n",
      "Epoch 10/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3197113.7500 - val_accuracy: 0.0000e+00 - val_loss: 2934796.7500\n",
      "Epoch 11/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3343713.0000 - val_accuracy: 0.0000e+00 - val_loss: 2971515.2500\n",
      "Epoch 12/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3013435.7500 - val_accuracy: 0.0000e+00 - val_loss: 2973740.0000\n",
      "Epoch 13/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3664778.7500 - val_accuracy: 0.0000e+00 - val_loss: 2538669.2500\n",
      "Epoch 14/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3316326.0000 - val_accuracy: 0.0000e+00 - val_loss: 2560841.2500\n",
      "Epoch 15/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3368492.2500 - val_accuracy: 0.0000e+00 - val_loss: 2756187.2500\n",
      "Epoch 16/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3491678.5000 - val_accuracy: 0.0000e+00 - val_loss: 3286073.7500\n",
      "Epoch 17/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3205881.5000 - val_accuracy: 0.0000e+00 - val_loss: 2571487.5000\n",
      "Epoch 18/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3017637.0000 - val_accuracy: 0.0000e+00 - val_loss: 2512877.0000\n",
      "Epoch 19/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3211288.2500 - val_accuracy: 0.0000e+00 - val_loss: 2386778.0000\n",
      "Epoch 20/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3144304.5000 - val_accuracy: 0.0000e+00 - val_loss: 2958380.7500\n",
      "Epoch 21/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3141612.7500 - val_accuracy: 0.0000e+00 - val_loss: 2877437.5000\n",
      "Epoch 22/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3144026.2500 - val_accuracy: 0.0000e+00 - val_loss: 2943772.7500\n",
      "Epoch 23/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3285175.0000 - val_accuracy: 0.0000e+00 - val_loss: 3423268.2500\n",
      "Epoch 24/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3295649.5000 - val_accuracy: 0.0000e+00 - val_loss: 2957887.5000\n",
      "Epoch 25/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3285968.7500 - val_accuracy: 0.0000e+00 - val_loss: 2232744.0000\n",
      "Epoch 26/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2988374.7500 - val_accuracy: 0.0000e+00 - val_loss: 3252814.5000\n",
      "Epoch 27/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3447868.0000 - val_accuracy: 0.0000e+00 - val_loss: 3148168.2500\n",
      "Epoch 28/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2974915.7500 - val_accuracy: 0.0000e+00 - val_loss: 3660251.7500\n",
      "Epoch 29/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3603136.2500 - val_accuracy: 0.0000e+00 - val_loss: 2690296.7500\n",
      "Epoch 30/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3091772.2500 - val_accuracy: 0.0000e+00 - val_loss: 2597465.0000\n",
      "Epoch 31/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3203703.5000 - val_accuracy: 0.0000e+00 - val_loss: 2790657.7500\n",
      "Epoch 32/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3544159.0000 - val_accuracy: 0.0000e+00 - val_loss: 2697292.2500\n",
      "Epoch 33/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3039898.0000 - val_accuracy: 0.0000e+00 - val_loss: 4060742.2500\n",
      "Epoch 34/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 3079242.7500 - val_accuracy: 0.0000e+00 - val_loss: 2383977.7500\n",
      "Epoch 35/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 3168014.2500 - val_accuracy: 0.0000e+00 - val_loss: 4559376.5000\n",
      "Epoch 36/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 3472699.5000 - val_accuracy: 0.0000e+00 - val_loss: 3537361.5000\n",
      "Epoch 37/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3114369.7500 - val_accuracy: 0.0000e+00 - val_loss: 2846914.5000\n",
      "Epoch 38/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3143703.0000 - val_accuracy: 0.0000e+00 - val_loss: 3022185.5000\n",
      "Epoch 39/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 7ms/step - accuracy: 0.0000e+00 - loss: 3467249.5000 - val_accuracy: 0.0000e+00 - val_loss: 3173194.0000\n",
      "Epoch 40/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 7ms/step - accuracy: 0.0000e+00 - loss: 3275552.5000 - val_accuracy: 0.0000e+00 - val_loss: 2267519.2500\n",
      "Epoch 41/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2979810.7500 - val_accuracy: 0.0000e+00 - val_loss: 2244190.5000\n",
      "Epoch 42/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3490411.7500 - val_accuracy: 0.0000e+00 - val_loss: 2513344.2500\n",
      "Epoch 43/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3368157.0000 - val_accuracy: 0.0000e+00 - val_loss: 3812008.2500\n",
      "Epoch 44/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3369159.7500 - val_accuracy: 0.0000e+00 - val_loss: 2687490.5000\n",
      "Epoch 45/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3162737.7500 - val_accuracy: 0.0000e+00 - val_loss: 2500924.0000\n",
      "Epoch 46/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.0000e+00 - loss: 2928575.2500 - val_accuracy: 0.0000e+00 - val_loss: 3135682.2500\n",
      "Epoch 47/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2991691.0000 - val_accuracy: 0.0000e+00 - val_loss: 2983063.5000\n",
      "Epoch 48/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3066047.5000 - val_accuracy: 0.0000e+00 - val_loss: 2873178.0000\n",
      "Epoch 49/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3074597.0000 - val_accuracy: 0.0000e+00 - val_loss: 2705841.0000\n",
      "Epoch 50/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3013288.5000 - val_accuracy: 0.0000e+00 - val_loss: 2678037.7500\n",
      "Epoch 51/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2943224.7500 - val_accuracy: 0.0000e+00 - val_loss: 2654437.0000\n",
      "Epoch 52/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2993307.5000 - val_accuracy: 0.0000e+00 - val_loss: 2658336.7500\n",
      "Epoch 53/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2846472.0000 - val_accuracy: 0.0000e+00 - val_loss: 2684085.0000\n",
      "Epoch 54/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3057831.5000 - val_accuracy: 0.0000e+00 - val_loss: 2955827.2500\n",
      "Epoch 55/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3144025.5000 - val_accuracy: 0.0000e+00 - val_loss: 2863588.2500\n",
      "Epoch 56/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2996281.7500 - val_accuracy: 0.0000e+00 - val_loss: 2618132.7500\n",
      "Epoch 57/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3089316.5000 - val_accuracy: 0.0000e+00 - val_loss: 2752273.7500\n",
      "Epoch 58/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2893752.2500 - val_accuracy: 0.0000e+00 - val_loss: 3017621.7500\n",
      "Epoch 59/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2997688.5000 - val_accuracy: 0.0000e+00 - val_loss: 2369327.5000\n",
      "Epoch 60/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2919437.0000 - val_accuracy: 0.0000e+00 - val_loss: 3004824.0000\n",
      "Epoch 61/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3408689.7500 - val_accuracy: 0.0000e+00 - val_loss: 2519989.5000\n",
      "Epoch 62/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2868306.2500 - val_accuracy: 0.0000e+00 - val_loss: 3142066.2500\n",
      "Epoch 63/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3411058.2500 - val_accuracy: 0.0000e+00 - val_loss: 2786327.5000\n",
      "Epoch 64/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2663223.5000 - val_accuracy: 0.0000e+00 - val_loss: 3219709.5000\n",
      "Epoch 65/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2751415.0000 - val_accuracy: 0.0000e+00 - val_loss: 2591006.5000\n",
      "Epoch 66/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3178633.7500 - val_accuracy: 0.0000e+00 - val_loss: 2934427.2500\n",
      "Epoch 67/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3025995.0000 - val_accuracy: 0.0000e+00 - val_loss: 2991407.5000\n",
      "Epoch 68/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3012271.0000 - val_accuracy: 0.0000e+00 - val_loss: 2590413.2500\n",
      "Epoch 69/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2937509.0000 - val_accuracy: 0.0000e+00 - val_loss: 2200973.0000\n",
      "Epoch 70/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2733340.5000 - val_accuracy: 0.0000e+00 - val_loss: 2425819.2500\n",
      "Epoch 71/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2801156.2500 - val_accuracy: 0.0000e+00 - val_loss: 2564425.5000\n",
      "Epoch 72/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2857440.2500 - val_accuracy: 0.0000e+00 - val_loss: 2969695.2500\n",
      "Epoch 73/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2955452.0000 - val_accuracy: 0.0000e+00 - val_loss: 2494573.2500\n",
      "Epoch 74/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3023957.7500 - val_accuracy: 0.0000e+00 - val_loss: 2944337.2500\n",
      "Epoch 75/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3027104.0000 - val_accuracy: 0.0000e+00 - val_loss: 2883902.0000\n",
      "Epoch 76/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 2943302.0000 - val_accuracy: 0.0000e+00 - val_loss: 2642034.5000\n",
      "Epoch 77/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 3253428.7500 - val_accuracy: 0.0000e+00 - val_loss: 2928154.0000\n",
      "Epoch 78/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 3119039.7500 - val_accuracy: 0.0000e+00 - val_loss: 4063312.2500\n",
      "Epoch 79/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3296115.0000 - val_accuracy: 0.0000e+00 - val_loss: 2665876.7500\n",
      "Epoch 80/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 2884487.0000 - val_accuracy: 0.0000e+00 - val_loss: 2577382.2500\n",
      "Epoch 81/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 8ms/step - accuracy: 0.0000e+00 - loss: 3033541.5000 - val_accuracy: 0.0000e+00 - val_loss: 3209472.0000\n",
      "Epoch 82/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 7ms/step - accuracy: 0.0000e+00 - loss: 2858115.0000 - val_accuracy: 0.0000e+00 - val_loss: 3027922.2500\n",
      "Epoch 83/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2792968.0000 - val_accuracy: 0.0000e+00 - val_loss: 3022149.2500\n",
      "Epoch 84/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2852334.2500 - val_accuracy: 0.0000e+00 - val_loss: 3741430.7500\n",
      "Epoch 85/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3100739.7500 - val_accuracy: 0.0000e+00 - val_loss: 2914001.2500\n",
      "Epoch 86/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2946148.0000 - val_accuracy: 0.0000e+00 - val_loss: 2428851.5000\n",
      "Epoch 87/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3013960.2500 - val_accuracy: 0.0000e+00 - val_loss: 2848357.2500\n",
      "Epoch 88/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3378998.5000 - val_accuracy: 0.0000e+00 - val_loss: 2932520.0000\n",
      "Epoch 89/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3027424.2500 - val_accuracy: 0.0000e+00 - val_loss: 2467963.0000\n",
      "Epoch 90/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2882048.5000 - val_accuracy: 0.0000e+00 - val_loss: 3430847.5000\n",
      "Epoch 91/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3142386.5000 - val_accuracy: 0.0000e+00 - val_loss: 2877445.5000\n",
      "Epoch 92/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3007272.7500 - val_accuracy: 0.0000e+00 - val_loss: 3302608.5000\n",
      "Epoch 93/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2941848.2500 - val_accuracy: 0.0000e+00 - val_loss: 2778433.0000\n",
      "Epoch 94/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3048050.7500 - val_accuracy: 0.0000e+00 - val_loss: 3025516.2500\n",
      "Epoch 95/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2958630.2500 - val_accuracy: 0.0000e+00 - val_loss: 3121510.7500\n",
      "Epoch 96/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2788601.5000 - val_accuracy: 0.0000e+00 - val_loss: 2610135.7500\n",
      "Epoch 97/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2859650.2500 - val_accuracy: 0.0000e+00 - val_loss: 2923383.2500\n",
      "Epoch 98/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2759916.7500 - val_accuracy: 0.0000e+00 - val_loss: 3280188.5000\n",
      "Epoch 99/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3193637.0000 - val_accuracy: 0.0000e+00 - val_loss: 2670223.7500\n",
      "Epoch 100/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2822700.2500 - val_accuracy: 0.0000e+00 - val_loss: 2523354.0000\n",
      "Epoch 101/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2920695.0000 - val_accuracy: 0.0000e+00 - val_loss: 2262675.0000\n",
      "Epoch 102/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2804890.5000 - val_accuracy: 0.0000e+00 - val_loss: 2898598.0000\n",
      "Epoch 103/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2797765.2500 - val_accuracy: 0.0000e+00 - val_loss: 3364428.5000\n",
      "Epoch 104/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3067932.2500 - val_accuracy: 0.0000e+00 - val_loss: 2474841.7500\n",
      "Epoch 105/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.0000e+00 - loss: 2931031.0000 - val_accuracy: 0.0000e+00 - val_loss: 3739258.2500\n",
      "Epoch 106/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2783812.2500 - val_accuracy: 0.0000e+00 - val_loss: 2558356.2500\n",
      "Epoch 107/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2886192.0000 - val_accuracy: 0.0000e+00 - val_loss: 2578111.2500\n",
      "Epoch 108/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2938261.2500 - val_accuracy: 0.0000e+00 - val_loss: 5629241.5000\n",
      "Epoch 109/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3079360.5000 - val_accuracy: 0.0000e+00 - val_loss: 3183802.7500\n",
      "Epoch 110/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2814152.2500 - val_accuracy: 0.0000e+00 - val_loss: 4000775.2500\n",
      "Epoch 111/300\n",
      "\u001b[1m 783/1532\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2888227.2500"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2944502.0000 - val_accuracy: 0.0000e+00 - val_loss: 2793885.5000\n",
      "Epoch 152/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2968128.7500 - val_accuracy: 0.0000e+00 - val_loss: 2305315.7500\n",
      "Epoch 153/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2713246.5000 - val_accuracy: 0.0000e+00 - val_loss: 2725833.2500\n",
      "Epoch 154/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2934588.7500 - val_accuracy: 0.0000e+00 - val_loss: 2479827.7500\n",
      "Epoch 155/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2644659.2500 - val_accuracy: 0.0000e+00 - val_loss: 5832155.0000\n",
      "Epoch 156/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2891913.2500 - val_accuracy: 0.0000e+00 - val_loss: 3171941.7500\n",
      "Epoch 157/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2873070.2500 - val_accuracy: 0.0000e+00 - val_loss: 2611037.2500\n",
      "Epoch 158/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.0000e+00 - loss: 2943693.0000 - val_accuracy: 0.0000e+00 - val_loss: 4235426.5000\n",
      "Epoch 159/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3450040.2500 - val_accuracy: 0.0000e+00 - val_loss: 2849670.7500\n",
      "Epoch 160/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3043920.0000 - val_accuracy: 0.0000e+00 - val_loss: 4027435.0000\n",
      "Epoch 161/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3338887.2500 - val_accuracy: 0.0000e+00 - val_loss: 2413410.0000\n",
      "Epoch 162/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2878118.0000 - val_accuracy: 0.0000e+00 - val_loss: 3963666.0000\n",
      "Epoch 163/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3199137.7500 - val_accuracy: 0.0000e+00 - val_loss: 2743167.2500\n",
      "Epoch 164/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 3136044.2500 - val_accuracy: 0.0000e+00 - val_loss: 3039534.5000\n",
      "Epoch 165/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.0000e+00 - loss: 2902668.5000 - val_accuracy: 0.0000e+00 - val_loss: 3011575.2500\n",
      "Epoch 166/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 8ms/step - accuracy: 0.0000e+00 - loss: 3074143.0000 - val_accuracy: 0.0000e+00 - val_loss: 3557651.0000\n",
      "Epoch 167/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3077945.5000 - val_accuracy: 0.0000e+00 - val_loss: 2893405.2500\n",
      "Epoch 168/300\n",
      "\u001b[1m1532/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 10ms/step - accuracy: 0.0000e+00 - loss: 2940009.2500 - val_accuracy: 0.0000e+00 - val_loss: 2956705.7500\n",
      "Epoch 169/300\n",
      "\u001b[1m1389/1532\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.0000e+00 - loss: 3219221.5000"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.pylab import style\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM, TimeDistributed, Dropout\n",
    "from tensorflow.python.keras.losses import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics\n",
    "\n",
    "pd.options.display.expand_frame_repr = False\n",
    "\n",
    "np.random.seed(7)\n",
    "\n",
    "style.use('ggplot')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams[\"figure.figsize\"] = (25, 10)\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['xtick.labelsize'] = 20\n",
    "plt.rcParams['ytick.labelsize'] = 20\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['lines.linewidth'] = 1\n",
    "Train_data = pd.read_csv('./data/used_car_train_20200313.csv', sep=' ')\n",
    "TestB_data = pd.read_csv('./data/used_car_testB_20200421.csv', sep=' ')\n",
    "\n",
    "## 输出数据的大小信息\n",
    "print('Train data shape:',Train_data.shape)\n",
    "print('TestB data shape:',TestB_data.shape)\n",
    "numerical_cols = Train_data.select_dtypes(exclude = 'object').columns\n",
    "categorical_cols = Train_data.select_dtypes(include = 'object').columns\n",
    "feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','creatDate','price','model','brand','regionCode','seller']]\n",
    "feature_cols = [col for col in feature_cols if 'Type' not in col]\n",
    "# 提前特征列，标签列构造训练样本和测试样本\n",
    "X_data = Train_data[feature_cols]\n",
    "Y_data = Train_data['price']\n",
    "\n",
    "X_test  = TestB_data[feature_cols]\n",
    "plt.hist(Y_data)\n",
    "# 缺省值用-1填补\n",
    "\n",
    "X_data = X_data.fillna(-1)\n",
    "X_test = X_test.fillna(-1)\n",
    "X_data = X_data.values.reshape((X_data.shape[0], 1, X_data.shape[1]))\n",
    "X_test = X_test.values.reshape((X_test.shape[0], 1, X_test.shape[1]))\n",
    "# x_train,x_val,y_train,y_val\n",
    "train = X_data[:-3000, :]\n",
    "test = X_data[-3000:, :]\n",
    "y_train=Y_data[:-3000]\n",
    "y_val=Y_data[-3000:]\n",
    "y_train\n",
    "model = Sequential()\n",
    "model.add(LSTM(64, input_shape=(X_data.shape[1], X_data.shape[2])))\n",
    "# model.add(LSTM(32))\n",
    "model.add(Dense(100))\n",
    "model.add(Dense(60))\n",
    "model.add(Dense(13))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "history = model.fit(train, y_train, epochs=150, batch_size=96, validation_data=(test, y_val), verbose=1)\n",
    "yHat = model.predict(test)\n",
    "inv_yHat = yHat\n",
    "inv_yHat\n",
    "inv_y = y_val\n",
    "inv_y\n",
    "def mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_pred - y_true) / y_true))\n",
    "\n",
    "print('MSE:',metrics.mean_squared_error(inv_yHat, inv_y))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(inv_yHat, inv_y)))\n",
    "print('MAE:',metrics.mean_absolute_error(inv_yHat, inv_y)) \n",
    "Y_test = model.predict(X_test)\n",
    "sub = pd.DataFrame()\n",
    "sub['SaleID'] = TestB_data.SaleID\n",
    "sub['price'] = Y_test\n",
    "sub.to_csv('sub_Weighted3.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2002886",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: nbconvert in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (7.16.4)\n",
      "Requirement already satisfied: beautifulsoup4 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (4.12.3)\n",
      "Requirement already satisfied: bleach!=5.0.0 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (6.1.0)\n",
      "Requirement already satisfied: defusedxml in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (0.7.1)\n",
      "Requirement already satisfied: importlib-metadata>=3.6 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (8.4.0)\n",
      "Requirement already satisfied: jinja2>=3.0 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (3.1.4)\n",
      "Requirement already satisfied: jupyter-core>=4.7 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (5.7.2)\n",
      "Requirement already satisfied: jupyterlab-pygments in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (0.3.0)\n",
      "Requirement already satisfied: markupsafe>=2.0 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (2.1.5)\n",
      "Requirement already satisfied: mistune<4,>=2.0.3 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (3.0.2)\n",
      "Requirement already satisfied: nbclient>=0.5.0 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (0.10.0)\n",
      "Requirement already satisfied: nbformat>=5.7 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (5.10.4)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from nbconvert) (24.1)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (1.5.1)\n",
      "Requirement already satisfied: pygments>=2.4.1 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (2.18.0)\n",
      "Requirement already satisfied: tinycss2 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (1.3.0)\n",
      "Requirement already satisfied: traitlets>=5.1 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbconvert) (5.14.3)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/lib/python3/dist-packages (from bleach!=5.0.0->nbconvert) (1.14.0)\n",
      "Requirement already satisfied: webencodings in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from bleach!=5.0.0->nbconvert) (0.5.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.9/dist-packages (from importlib-metadata>=3.6->nbconvert) (3.20.1)\n",
      "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.9/dist-packages (from jupyter-core>=4.7->nbconvert) (4.2.2)\n",
      "Requirement already satisfied: jupyter-client>=6.1.12 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbclient>=0.5.0->nbconvert) (8.6.2)\n",
      "Requirement already satisfied: fastjsonschema>=2.15 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbformat>=5.7->nbconvert) (2.20.0)\n",
      "Requirement already satisfied: jsonschema>=2.6 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from nbformat>=5.7->nbconvert) (4.23.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from beautifulsoup4->nbconvert) (2.6)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert) (24.2.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert) (2023.12.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert) (0.35.1)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert) (0.20.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.9/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (2.9.0.post0)\n",
      "Requirement already satisfied: pyzmq>=23.0 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (26.2.0)\n",
      "Requirement already satisfied: tornado>=6.2 in /home/jovyan/.virtualenvs/basenv/lib/python3.9/site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.4.1)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install nbconvert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2584d95d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
